1. Field of Invention
The present invention relates to a system and method for conducting trade activities and more particularly to a system and method for conducting trade activities electronically with the capability of achieving and optimizing complex trade objectives in the realm of electronic commerce.
2. Discussion of Prior Art
Current electronic commerce systems lack the decision support capabilities necessary for achieving the objectives of the various traders, especially in business-to-business electronic transactions. For example:
Procurement Organization. A business or government agency may seek to perform a multi-million dollar procurement of various office supplies from a possibly large number of authorized suppliers. An example of a procurement objective is to minimize the total expenditure on the required quantities of office supplies, under the limitations of the allocated budget, and the maximal price per specific items the agency is ready to pay. It is desirable that the underlying E-commerce system would recommend the optimal trade, i.e., what items and in what quantities should be purchased from each authorized supplier and for what price. Buying each item from a supplier offering the minimal price per item may not be the best strategy, because of various deals, incentives and volume discounts that suppliers may be willing to offer.
Supplier. A computer hardware supplier offers a range of components and their configurations. One possible objective is maximizing its revenue, while maintaining at least a 17% profit margin, subject to limitations on the current inventory levels and capacity, and under the requirement that inventory turnover be at least 50% per month. Also, a supplier may be willing to offer numerous special deals and incentives to preferred volume buyers.
Manufacturer. A pharmaceutical manufacturer may seek to perform a complex transaction of selling a bundle of its products to a chain of drug stores, and, at the same time, purchasing a range of raw materials necessary to manufacture them. In doing so, the manufacturer may be trying to achieve the objective of maximizing the overall profit subject to the limitations on manufacturing production capacity, available manufacturing processes and the available cash.
Collaborating Bidder. An authorized (e.g., on a GSA schedule) supplier (or manufacturer) is willing to put a bid in response to a big procurement solicitation by the federal government. The supplier may be too small to respond to large-scale solicitation, and he may seek to find a bidding alliance with other complementary suppliers. An example objective of the supplier may be to minimize the combined bid price (to increase the chances of winning), while guaranteeing his own 13% profit margin and under the restriction that his expenses shall not exceed $2 million.
Surplus Seller. An electronic device manufacturer may seek to eliminate useless surplus inventory. The objective here may be to maximize the sale price for the overall surplus, possibly selling it to more than one buyer.
For decision support, corporations with large volume of business transactions maintain extensive operations and RandD staff, as well as special-purpose, often proprietary, decision-support systems. However, the development of such special-purpose systems requires tremendous RandD effort in terms of time and capital outlay. Furthermore, those special purpose systems are typically not adaptable when it comes to dynamic evolutionary changes in business structure, constraints and objectives. Moreover, even in large corporations, many of the decision support activities, such as in the above examples, are not automated. Most importantly, special purpose systems are not capable of supporting transactions that span across widely distributed suppliers, manufacturers, and procurement organizations. On the other side of the spectrum, many small and medium size companies and organizations simply cannot afford the luxury of maintaining large sales and procurement staff and the special-purpose decision support tools. Those companies cannot keep up with ever-changing business opportunities, which often involve numerous business parties engaged in electronic commerce.
Companies such as Ariba, CommerceOne, Commerce Exchange, etc., do provide procurement and supply side integration, but the decision of exactly which items need to be purchased or sold, from or to which trader, and in what quantities and for what prices is left to sales and procurement personnel. Also lacking matchmaking optimization capabilities are Internet-based electronic commerce services, such as electronic malls and shops (e.g., IMALL and Amazon.com), electronic auctions (e.g., EBAY and Yahoo), and competitive shopping (e.g., PriceLine.com, using a reverse auction). Today, companies in that category mainly provide business-to-consumer and consumer-to-consumer services, but are also trying to expand into the business-to-business market. Products like IBM Net.Commerce and MS Site Server are suites of software productivity tools used to deploy a wide range of E-commerce solutions. However, they also lack the decision support capabilities necessary for achieving complex trade objectives.
Current Internet-based trade systems only support simple trade objectives such as purchasing or selling specific items within a certain price range. For example, EBAY allows the auctioning of specific items, i.e., iterative price-bids bounded by a floor price and a time deadline. IMALL supports selling specific products or services at a fixed price. PriceLine.com allows customers to bid their own price for a product or service, does comparative shopping and keeps the monetary difference.
Prior art examples of systems and methods used in connection with electronic commerce, trade optimization and logistics support are disclosed in various US Patents and related literature.
U.S. Pat. No. 4,903,201 discloses a computerized automated futures trading exchange. The traders in the exchange enter bids to purchase commodity contracts. They also enter offers to sell commodity contracts The system automatically matches between bids and offers. The system automatically completes transactions between traders.
The invention above lacks the capability to match an aggregation of partial bids to an aggregation of partial offers, where bids and offers are specified as ranges delimited by constraints. In the invention above the trader lacks the capability to define an objective function and to perform optimization on the specified objective function. The invention above is limited to the futures markets.
U.S. Pat. No. 5,077,665 discloses a matching system in which bids are automatically matched against offers for given trading instruments. Although the system provides match making between bids and offers of financial instruments, the system does not provide the trader the ability to specify objective function, to set constraints per specific financial instrument, and therefore to achieve a predefined business objective. The invention described therein is related only to financial markets and does not allow the user to specify other items for match making besides financial instruments.
U.S. Pat. No. 5,283,731 discloses computer-based classified advertising. The system comprises a data processor and means for creating an advertising database available to each user in the system. The invention described therein restricts the matching capabilities to a single match and does not provide capabilities to perform optimization and to specify complex trading specifications, constraints and objectives.
U.S. Pat. No. 5,710,887 discloses a computer system and method for electronic commerce. The system facilitates commercial transactions between a plurality of customers and at least one supplier of items over a computer driven network capable of providing communications between the supplier and at least one customer site associated with each customer. Despite the fact that the system disclosed in the invention is suitable for a wide range of providers of goods and services, it does not posses the ability to specify particular items in a precise way, or to perform optimized match making. The invention described therein describes various business paradigms for electronic commerce, but does not allow performing xe2x80x9cOne-to-onexe2x80x9d or xe2x80x9cOne-To-Manyxe2x80x9d electronic transactions based on optimized match making. In addition, the invention described therein does not allow specification of constraints on specific item parameters.
Another area within the prior art describes various optimization methods and systems, using mostly linear optimization methods. These inventions, although providing optimization tools for business transactions, do not allow users to specify parameters of traded items in a flexible way, do not allow specifications of constraints on specific parameters of a traded item, and do not allow users to perform One-to-One, and One-to-Many transactions.
U.S. Pat. No. 5,630,070 discloses the method for optimization of resources planning. The method described in the invention provides for an optimization of a manufacturing process by designating the amounts of various manufactured products to be produced. In order to accomplish optimization, the method employs an objective function such as maximization of income in a situation where there are limitations on the inventory of raw materials and on the tools employed in the manufacturing process. The method does not allow specifying unique constraints on specific items participating in the manufacturing process. The method does not allow performing multiple transactions and does not allow performing match making of consumers"" items with suppliers"" items.
All previous inventions describing various methods for manufacturing logistic decision support receive as input a bill of materials or a predefined set of the goods or subassemblies. They do not offer the flexibility of choosing different vendors of subassemblies through a sophisticated match making mechanism.
U.S. Pat. No. 5,450,317 discloses a method and system for optimized logistics planning. The invention described therein recommends optimal order quantities and timing, choice of vendor locations and storage locations, and transportation models, for individual items and for product families. The invention does not allow using a match making mechanism to select vendors. The invention allows for specification of fixed parameters for customers and suppliers, rather than parameters expressed through constraints.
Summarizing the examples of the inventions described above, it is clear that none of them provides a unified way to perform optimized match making trading activities in the realm of electronic commerce. It is, therefore, an object of the invention to provide an Adaptive Trade Specification (ATS) model for using in electronic commerce realm.
It is further object of invention to provide an ATS based match making and optimization automated method that can find optimal trade transaction for variety of users in electronic commerce domain.
It is an advantage of the invention in comparison with prior art that match making and optimization are combined under one ATS based mechanism which allows traders to design transactions that are optimal in terms of trader""s objectives and which are mutually agreeable with available trade specifications
The invention allows various traders to achieve optimal trade transactions. First, it provides the Adaptive Trade Specification (ATS) model. The ATS model allows to describe, in a precise and uniform way, trade parameters, constraints and objectives for a wide range of of traders, including procurement organizations, suppliers, manufacturers, resellers, surplus sellers, trade-in sellers, stock marker traders, general buyers and sellers, etc. Second, given a trader""s ATS, the invention provides an automated process that recommends specific transactions with other traders"" ATS""s, that are mutually agreeable with, and optimize the to objective of, the trader""s ATS (e.g. minimal price, maximal profit, etc.). More specifically, the invention comprises the following components:
Adaptive Trade Specification (ATS) Model. Adaptive Trade Specification (ATS) is a formal mathematical description of trader""s objective and constraints, such as in the examples in the prior art section. ATS constraints include restrictions (on quantities, prices, totals, profits, revenues etc.) that must be satisfied to perform an optimal transaction, and the interconnection between various business parameters (such as profit, quantities, prices and costs). The core of each ATS is a specification of xe2x80x9citemsxe2x80x9d the trader offers to GIVE as well as xe2x80x9citemsxe2x80x9d to TAKE in return. For example, a procurement organization may offer to GIVE the xe2x80x9citemxe2x80x9d money and wants to TAKE items of office supply. An office equipment supplier may have an ATS, in which all its catalog appears as GIVE items, and money as the only TAKE item. Whereas, a manufacturer may have an ATS, in which all of its products appear as GIVE items, all raw materials and money (i.e., revenues for its products) as TAKE items. ATS is adaptive in that various numeric parameters such as quantities of items, prices, profit, revenue, totals etc. are not fixed, but could vary, provided that they satisfy the ATS constraints. Item specifications in an ATS are also constraint-based and not fixed. For example, an ATS of a trader may include, as one of the TAKE item specifications, a hard disk that has at least 12 GB capacity and is compatible with a G7305E mother board; no exact model or vendor is necessary. The ATS model provides a uniform and expressive way to capture any conceivable trades that can be formulated in terms of given and taken items. To help traders in the definition of an ATS, a library of specialized wizards (i.e., specialized xe2x80x9csmartxe2x80x9d interface templates) can be used for various types of traders (e.g., suppliers, procurement organizations, manufacturers etc.), as in the examples in the Prior Art section. For each type of trader, the wizard would automatically construct an ATS from the user given set of trading parameters relevant to a trading scenario. The trader who uses a wizard would not need to understand the mathematical description of an ATS, but rather trading parameters and concepts that are familiar to the trader (e.g. availability, quantity, price, revenue, etc.). However, the description of wizard library is described elsewhere in a complementary patent application cited above, and is not intended as a limitation on the present invention.
ATS-based Match Making (MM) Optimization Methods. Given a trader""s ATS, the MM optimization methods recommend specific transactions with other traders (i.e., against their ATS""s) that are mutually agreeable and optimize the objective of the trader""s ATS (e.g., minimal price, maximal profit etc.). The recommended set of transactions will indicate exactly with whom the transaction should be made, the exact GIVE and TAKE items and their quantities, as well as other relevant parameters (e.g., price and profit). For example, for a procurement ATS (i.e., that originates from a procurement trader), the MM optimization methods recommend a set of suppliers"" ATS""s and the exact quantities of the items to be purchased from each, so that the procurement ATS objective, say the minimal total cost, is achieved. Or, for a manufacturer""s ATS, the MM optimization methods can recommend a set of ATS""s of buyers interested in the manufacturer""s products, and a set of ATS""s of suppliers of raw materials, which are necessary to manufacture the products, so that the manufacturer""s objective, say maximal profit, is achieved. The ATS-based match making and optimization are generic and work uniformly regardless of a specific wizard (or trader type) that generated them. Four exemplary MM optimization methods are set forth herein: 1. generic MM optimization with any number of committed ATS""s and one optimization objective; 2. One-to-All MM optimization which has one optimizing ATS (i.e., whose objective is used for optimization) and which recommends a (multiple) transaction that may involve some or all of the committed ATS""s; 3. One-to-One MM optimization, which has one optimizing ATS and recommends a transaction that may involve exactly one committed ATS; and 4. One-to-K MM optimization, where K is an integer number, which has one optimizing ATS and which recommends a multiple transaction that may involve K or less committed ATS""s.